Pakistan

The widespread penetration of hydrogen in mainstream energy systems requires hydrogen production processes to be economically competent and environmentally efficient. Hydrogen if produced efficiently can play a pivotal role in decarbonizing the global energy systems. Therefore this study develops a framework which evaluates hydrogen production processes and quantifies deficiencies for improvement. The framework integrates slack-based data envelopment analysis (DEA) with fuzzy analytical hierarchy process (FAHP) and fuzzy technique for order of preference by similarity to ideal solution (FTOPSIS). The proposed framework is applied to prioritize the most efficient and sustainable hydrogen production in Pakistan. Eleven hydrogen production alternatives were analyzed under five criteria including capital cost feedstock cost O&M cost hydrogen production and CO2 emission. FAHP obtained the initial weights of criteria while FTOPSIS determined the ultimate weights of criteria for each alternative. Finally slack-based DEA computed the efficiency of alternatives. Among the 11 three alternatives (wind electrolysis PV electrolysis and biomass gasification) were found to be fully efficient and therefore can be considered as sustainable options for hydrogen production in Pakistan. The rest of the eight alternatives achieved poor efficiency scores and thus are not recommended.

To address climate change energy security and waste management new sustainable energy sources must be developed. This study uses Aspen Plus software to extract bio-H2 from food waste with the goal of efficiency and environmental sustainability. Anaerobic digestion optimised to operate at 20-25°C and keep ammonia at 3% greatly boosted biogas production. The solvent [Emim][FAP] which is based on imidazolium had excellent performance in purifying biogas. It achieved a high level of methane purity while consuming a minimal amount of energy with a solvent flow rate of 13.415 m³/h. Moreover the utilization of higher temperatures (600-700°C) during the bio-H2 generation phase significantly enhanced both the amount and quality of hydrogen produced. Parametric and sensitivity assessments were methodically performed at every stage. This integrated method was practicable and environmentally friendly according to the economic assessment. H2 generation using steam reforming results in a TCC of 1.92×106 USD. The CO2 separation step has higher costs (TCC of 2.15×107 USD) due to ionic liquid washing and CO2 liquefaction. Compressor electricity consumption significantly impacts total operating cost (TOC) totaling 4.73×108 USD. showing its ability to reduce greenhouse gas emissions optimize resource utilization and promote energy sustainability. This study presents a sustainable energy solution that addresses climate and waste challenges.

In the ongoing effort to reduce carbon emissions on a worldwide scale green hydrogen which is generated through environmentally responsible processes has emerged as a significant driving force. As the demand for clean energy continues to rise it is becoming increasingly important to have a solid understanding of the technological and economic elements of modern techniques of producing green hydrogen. In the context of green hydrogen generation understanding green hydrogen production's techno-economic features is necessary to reduce carbon emissions and transition to a low-carbon economy. associated with breakthroughs in technology the present study examines the most fascinating and relevant aspects of techno-economic analysis. Despite challenges green hydrogen can help the world move to a cleaner more sustainable energy future with solid analytical frameworks and legislation.

The integration of Variable Renewable Energy (VRE) sources in power systems is increased for a sustainable environment. However due to the intermittent nature of VRE sources formulating efficient economic dispatching strategies becomes challenging. This systematic review aims to elucidate the economic value creation of Artificial Intelligence (AI) in supporting the integration of VRE sources into power systems by reviewing the role of AI in mitigating costs related to balancing profile and grid with a focus on its applications for generation and demand forecasting market design demand response storage solutions power quality enhancement and predictive maintenance. The proposed study evaluates the AI potential in economic efficiency and operational reliability improvement by analyzing the use cases with various Renewable Energy Resources (RERs) including wind solar geothermal hydro ocean bioenergy hydrogen and hybrid systems. Furthermore the study also highlights the development and limitations of AI-driven approaches in renewable energy sector. The findings of this review aim to highlight AI’s critical role in optimizing VRE integration ultimately informing policymakers researchers and industry stakeholders about the potential of AI for an economically sustainable and resilient energy infrastructure.